[1]翟永杰,杨旭,赵振兵,等.融合共现推理的Faster R-CNN输电线路金具检测[J].智能系统学报,2021,16(2):237-246.[doi:10.11992/tis.202012023]
ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16(2):237-246.[doi:10.11992/tis.202012023]
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ZHAI Yongjie,YANG Xu,ZHAO Zhenbing,et al.Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection[J].CAAI Transactions on Intelligent Systems,2021,16(2):237-246.[doi:10.11992/tis.202012023]
融合共现推理的Faster R-CNN输电线路金具检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第2期
页码:
237-246
栏目:
学术论文—机器学习
出版日期:
2021-03-05
- Title:
- Integrating co-occurrence reasoning for Faster R-CNN transmission line fitting detection
- Keywords:
- transmission lines; fitting; deep learning; object detection; Faster R-CNN; structured assembly; co-occurrence matrix; co-occurrence reasoning module
- 分类号:
- TP18
- 摘要:
- 为促进目标检测模型与电力领域业务知识有机融合，缓解金具样本间长尾分布问题，有效提升输电线路金具的自动化检测效果，提出了融合共现推理的Faster R-CNN(faster region-based convolutional neural network)输电线路金具检测模型。首先，深入研究输电线路金具结构化组合规则，通过数据驱动的方式以条件概率对金具目标间的共现连接关系进行有效表达；然后，结合图学习方法，利用学习并映射的共现概率关联作为共现图邻接矩阵，使用基线模型(Faster R-CNN)提取的特征向量作为图推理输入特征，以自学习的变换矩阵作为共现图传播权重，完成图信息传播并实现有效的共现推理模型嵌入。实验证明，融合共现推理模块的Faster R-CNN模型较原始模型提升了6.56%的准确率，对于长尾分布样本中数量较少的金具性能提升尤其显著。
- Abstract:
- To promote the organic integration of object detection and business knowledge in the electric power field, alleviate the problem of long-tailed distribution among fitting samples, and effectively improve the automatic detection effect of transmission line fittings, we propose a faster region-based convolutional neural network (Faster R-CNN) transmission line fitting detection model based on integrating co-occurrence reasoning. First, the structural combination rule of transmission line fittings is extensively investigated, and the co-occurrence connection relationship between objects is effectively expressed using conditional probability in a data-driven manner. Then, in combination with the graph learning method, the co-occurrence probability association is learned and mapped as the adjacency matrix of the co-occurrence graph, the feature vector extracted from the baseline model (Faster R-CNN) is used as the graph inference input feature, and the self-learning transformation matrix is used as the propagation weight of the co-occurrence graph to complete graph information propagation and realize effective co-occurrence inference model embedding. The experimental results show that the Faster R-CNN integrating co-occurrence reasoning module outperforms the original model by 6.56%, which is particularly significant for performance improvement in terms of transmission line fitting detection with a reduced long-tailed distribution among fitting samples.
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备注/Memo
收稿日期:2020-12-15。
基金项目:国家自然科学基金项目（61871182、61773160）；北京市自然科学基金项目（4192055）；河北省自然科学基金项目（F2020502009）
作者简介:翟永杰，教授，博士，主要研究方向为电力视觉。主持国家自然科学基金面上项目1项，河北省自然科学基金项目1项，主持横向科研项目多项，参与国家重点研发计划项目1项，获山东省科技进步一等奖1项。发表论文30余篇，授权发明专利10项，编著1部，参编教材1部、著作3部;杨旭，硕士研究生，主要研究方向为电力视觉与人工智能;赵振兵，教授，博士，主要研究方向为电力视觉。主持国家自然科学基金等纵向课题10项；获省科技进步一等奖1项(第3完成人)；以第1完成人获得国家专利授权16项；以第1作者出版专著2部，发表学术论文30余篇
通讯作者:赵振兵.E-mail:zhaozhenbing@ncepu.edu.cn
更新日期/Last Update:
2021-04-25